Homework 0 Key (not to be handed in) due? Jan. 10

Homework 0 Key (not to be handed in) due? Jan. 10 The results of running diamond.sas is listed below: Note: I did slightly reduce the size of some of the graphs so that they would fit on the page. The SAS System Obs weight price 1 0.17 355 2 0.16 328 3 0.17 350 4 0.18 325 5 0.25 642 6 0.16 342 7 0.15 322 8 0.19 485 9 0.21 483 10 0.15 323 11 0.18 462 12 0.28 823 13 0.16 336 14 0.20 498 15 0.23 595 16 0.29 860 17 0.12 223 18 0.26 663 19 0.25 750 20 0.27 720 21 0.18 468 22 0.16 345 23 0.17 352 24 0.16 332 25 0.17 353 26 0.18 438 1

Obs weight price 27 0.17 318 28 0.18 419 29 0.17 346 30 0.15 315 31 0.17 350 32 0.32 918 33 0.32 919 34 0.15 298 35 0.16 339 36 0.16 338 37 0.23 595 38 0.23 553 39 0.17 345 40 0.33 945 41 0.25 655 42 0.35 1086 43 0.18 443 44 0.25 678 45 0.25 675 46 0.15 287 47 0.26 693 48 0.15 316 49 0.43. 2

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Diamond Ring Price Study Scatter plot of Price vs. Weight with Regression Line The REG Procedure Model: MODEL1 Dependent Variable: price Number of Observations Read 49 Number of Observations Used 48 Number of Observations with Missing Values 1 Source DF Sum of Squares Analysis of Variance Mean Square F Value Pr > F Model 1 2098596 2098596 2069.99 <.0001 Error 46 46636 1013.81886 Corrected Total 47 2145232 Root MSE 31.84052 R-Square 0.9783 Dependent Mean 500.08333 Adj R-Sq 0.9778 Coeff Var 6.36704 Variable DF Parameter Estimate Parameter Estimates Standard Error t Value Pr > t 95% Confidence Limits Intercept 1-259.62591 17.31886-14.99 <.0001-294.48696-224.76486 weight 1 3721.02485 81.78588 45.50 <.0001 3556.39841 3885.65129 5

Diamond Ring Price Study Scatter plot of Price vs. Weight with Regression Line The REG Procedure Model: MODEL1 Dependent Variable: price Obs weight Dependent Variable Predicted Value Output Statistics Std Error Mean Predict Residual Std Error Residual 6 Student Residual -2-1 0 1 2 Cook's D 1 0.17 355.0000 372.9483 5.3786-17.9483 31.383-0.572 * 0.005 2 0.16 328.0000 335.7381 5.8454-7.7381 31.299-0.247 0.001 3 0.17 350.0000 372.9483 5.3786-22.9483 31.383-0.731 * 0.008 4 0.18 325.0000 410.1586 5.0028-85.1586 31.445-2.708 ***** 0.093 5 0.25 642.0000 670.6303 5.9307-28.6303 31.283-0.915 * 0.015 6 0.16 342.0000 335.7381 5.8454 6.2619 31.299 0.200 0.001 7 0.15 322.0000 298.5278 6.3833 23.4722 31.194 0.752 * 0.012 8 0.19 485.0000 447.3688 4.7396 37.6312 31.486 1.195 ** 0.016 9 0.21 483.0000 521.7893 4.6205-38.7893 31.503-1.231 ** 0.016 10 0.15 323.0000 298.5278 6.3833 24.4722 31.194 0.785 * 0.013 11 0.18 462.0000 410.1586 5.0028 51.8414 31.445 1.649 *** 0.034 12 0.28 823.0000 782.2611 7.7193 40.7389 30.891 1.319 ** 0.054 13 0.16 336.0000 335.7381 5.8454 0.2619 31.299 0.00837 0.000 14 0.20 498.0000 484.5791 4.6084 13.4209 31.505 0.426 0.002 15 0.23 595.0000 596.2098 5.0582-1.2098 31.436-0.0385 0.000 16 0.29 860.0000 819.4713 8.3905 40.5287 30.715 1.320 ** 0.065 17 0.12 223.0000 186.8971 8.2768 36.1029 30.746 1.174 ** 0.050 18 0.26 663.0000 707.8406 6.4787-44.8406 31.174-1.438 ** 0.045 19 0.25 750.0000 670.6303 5.9307 79.3697 31.283 2.537 ***** 0.116 20 0.27 720.0000 745.0508 7.0789-25.0508 31.044-0.807 * 0.017 21 0.18 468.0000 410.1586 5.0028 57.8414 31.445 1.839 *** 0.043 22 0.16 345.0000 335.7381 5.8454 9.2619 31.299 0.296 0.002 23 0.17 352.0000 372.9483 5.3786-20.9483 31.383-0.668 * 0.007 24 0.16 332.0000 335.7381 5.8454-3.7381 31.299-0.119 0.000 25 0.17 353.0000 372.9483 5.3786-19.9483 31.383-0.636 * 0.006

Obs weight Dependent Variable Predicted Value Output Statistics Std Error Mean Predict Residual Std Error Residual Student Residual -2-1 0 1 2 Cook's D 26 0.18 438.0000 410.1586 5.0028 27.8414 31.445 0.885 * 0.010 27 0.17 318.0000 372.9483 5.3786-54.9483 31.383-1.751 *** 0.045 28 0.18 419.0000 410.1586 5.0028 8.8414 31.445 0.281 0.001 29 0.17 346.0000 372.9483 5.3786-26.9483 31.383-0.859 * 0.011 30 0.15 315.0000 298.5278 6.3833 16.4722 31.194 0.528 * 0.006 31 0.17 350.0000 372.9483 5.3786-22.9483 31.383-0.731 * 0.008 32 0.32 918.0000 931.1020 10.5294-13.1020 30.049-0.436 0.012 33 0.32 919.0000 931.1020 10.5294-12.1020 30.049-0.403 0.010 34 0.15 298.0000 298.5278 6.3833-0.5278 31.194-0.0169 0.000 35 0.16 339.0000 335.7381 5.8454 3.2619 31.299 0.104 0.000 36 0.16 338.0000 335.7381 5.8454 2.2619 31.299 0.0723 0.000 37 0.23 595.0000 596.2098 5.0582-1.2098 31.436-0.0385 0.000 38 0.23 553.0000 596.2098 5.0582-43.2098 31.436-1.375 ** 0.024 39 0.17 345.0000 372.9483 5.3786-27.9483 31.383-0.891 * 0.012 40 0.33 945.0000 968.3123 11.2709-23.3123 29.779-0.783 * 0.044 41 0.25 655.0000 670.6303 5.9307-15.6303 31.283-0.500 0.004 42 0.35 1086 1043 12.7819 43.2672 29.162 1.484 ** 0.211 43 0.18 443.0000 410.1586 5.0028 32.8414 31.445 1.044 ** 0.014 44 0.25 678.0000 670.6303 5.9307 7.3697 31.283 0.236 0.001 45 0.25 675.0000 670.6303 5.9307 4.3697 31.283 0.140 0.000 46 0.15 287.0000 298.5278 6.3833-11.5278 31.194-0.370 0.003 47 0.26 693.0000 707.8406 6.4787-14.8406 31.174-0.476 0.005 48 0.15 316.0000 298.5278 6.3833 17.4722 31.194 0.560 * 0.007 49 0.43. 1340 19.0332.... Sum of Residuals 0 Sum of Squared Residuals 46636 Predicted Residual SS (PRESS) 50738 7

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Diamond Ring Price Study Scatter plot of Price vs. Weight with Regression Line Obs weight price pred resid 1 0.17 355 372.95-17.9483 2 0.16 328 335.74-7.7381 3 0.17 350 372.95-22.9483 4 0.18 325 410.16-85.1586 5 0.25 642 670.63-28.6303 6 0.16 342 335.74 6.2619 7 0.15 322 298.53 23.4722 8 0.19 485 447.37 37.6312 9 0.21 483 521.79-38.7893 10 0.15 323 298.53 24.4722 11 0.18 462 410.16 51.8414 12 0.28 823 782.26 40.7389 13 0.16 336 335.74 0.2619 14 0.20 498 484.58 13.4209 15 0.23 595 596.21-1.2098 16 0.29 860 819.47 40.5287 17 0.12 223 186.90 36.1029 18 0.26 663 707.84-44.8406 19 0.25 750 670.63 79.3697 20 0.27 720 745.05-25.0508 21 0.18 468 410.16 57.8414 22 0.16 345 335.74 9.2619 23 0.17 352 372.95-20.9483 24 0.16 332 335.74-3.7381 25 0.17 353 372.95-19.9483 26 0.18 438 410.16 27.8414 27 0.17 318 372.95-54.9483 28 0.18 419 410.16 8.8414 11

Obs weight price pred resid 29 0.17 346 372.95-26.9483 30 0.15 315 298.53 16.4722 31 0.17 350 372.95-22.9483 32 0.32 918 931.10-13.1020 33 0.32 919 931.10-12.1020 34 0.15 298 298.53-0.5278 35 0.16 339 335.74 3.2619 36 0.16 338 335.74 2.2619 37 0.23 595 596.21-1.2098 38 0.23 553 596.21-43.2098 39 0.17 345 372.95-27.9483 40 0.33 945 968.31-23.3123 41 0.25 655 670.63-15.6303 42 0.35 1086 1042.73 43.2672 43 0.18 443 410.16 32.8414 44 0.25 678 670.63 7.3697 45 0.25 675 670.63 4.3697 46 0.15 287 298.53-11.5278 47 0.26 693 707.84-14.8406 48 0.15 316 298.53 17.4722 49 0.43. 1340.41. 12

log window NOTE: Copyright (c) 2002-2010 by SAS Institute Inc., Cary, NC, USA. NOTE: SAS (r) Proprietary Software 9.3 (TS1M1) Licensed to PURDUE UNIVERSITY - T&R, Site 70085364. NOTE: This session is executing on the X64_7PRO platform. NOTE: Updated analytical products: SAS/STAT 9.3_M1, SAS/ETS 9.3_M1, SAS/OR 9.3_M1 NOTE: SAS initialization used: 10.74 seconds 1.45 seconds 1 *If you are running version 9.3 locally (either on your personal computer or on 2 an ITAP computer), the following will reset the output. The lines will NOT work 3 if you are using goremote.; 4 ods html close; 5 ods html; NOTE: Writing HTML Body file: sashtml.htm 6 13

7 *The following linesize (ls) and pagesize (ps) options MAY work 8 well if you have your print setup (click file, print setup) 9 with 0.5 in margins and portrait selected on page setup and 10 and SAS Monospace, Roman, size 8 selected on font. The 11 print setup display will tell you the ls and ps for the 12 selections you have chosen. Some printers may be a little 13 different and you may need to play with these settings. ; 14 15 options ls=105 ps=60 nocenter; 16 17 *Read in the data using the cards (datalines) statement. The @@ allows more 18 than one case per line. The lone. represents a missing value 19 and we can use this for prediction of price at that weight; 20 data diamonds; input weight price @@; 21 cards; NOTE: SAS went to a new line when INPUT statement reached past the end of a line. NOTE: The data set WORK.DIAMONDS has 49 observations and 2 variables. NOTE: DATA statement used (Total process time): 0.77 seconds 0.03 seconds 29 ; 30 31 *Create new data set that does not include the last case (we do 32 this for plotting purposes since we don't want 0.43 included 33 on the x-axis in our plots); 34 data diamonds1; set diamonds; if price ne.; 35 36 *Print the data set diamonds1; NOTE: There were 49 observations read from the data set WORK.DIAMONDS. NOTE: The data set WORK.DIAMONDS1 has 48 observations and 2 variables. NOTE: DATA statement used (Total process time): 0.02 seconds 0.01 seconds 37 proc print data=diamonds; run; NOTE: There were 49 observations read from the data set WORK.DIAMONDS. NOTE: PROCEDURE PRINT used (Total process time): 0.29 seconds 0.03 seconds 38 39 *Sort the data according to weight (if we don't, the smoothing 40 curve on our plot will not work correctly); 41 proc sort data=diamonds1; by weight; 42 43 *Generate a scatterplot with smooth curve fitted to 44 the data. Note that there are several preceding statements 45 that can be used to title the plot and axes.; 46 symbol1 v=circle i=sm70; 47 title1 'Diamond Ring Price Study'; 48 title2 'Scatter plot of Price vs. Weight with Smoothing Curve'; 49 axis1 label=('weight (Carats)'); 50 axis2 label=(angle=90 'Price (Singapore $$)'); 14

NOTE: There were 48 observations read from the data set WORK.DIAMONDS1. NOTE: The data set WORK.DIAMONDS1 has 48 observations and 2 variables. NOTE: PROCEDURE SORT used (Total process time): 0.39 seconds 0.01 seconds 51 proc gplot data=diamonds1; 52 plot price*weight / haxis=axis1 vaxis=axis2; 53 run; NOTE: Input data contained multiple vertical values for individual horizontal values. Parametric fit is required to force curve through individual observations. NOTE: 4 records written to D:\Users\lfindsen\gplot.png. 54 55 *To copy plots from SAS to WORD: (1) In SAS, select the plot, 56 right click and choose COPY. (2) In WORD, put the cursor in the 57 desired location, PASTE SPECIAL and select 58 "HTML Format". 59 60 *We can also make a plot with a regression line; 61 symbol1 v=circle i=rl; 62 title2 'Scatter plot of Price vs. Weight with Regression Line'; NOTE: There were 48 observations read from the data set WORK.DIAMONDS1. NOTE: PROCEDURE GPLOT used (Total process time): 2.03 seconds 0.32 seconds 63 proc gplot data=diamonds1; 64 plot price*weight / haxis=axis1 vaxis=axis2; 65 run; NOTE: Regression equation : price = -259.6259 + 3721.025*weight. NOTE: 5 records written to D:\Users\lfindsen\gplot1.png. 66 67 *Perform regression analysis using data set 'diamonds'. The clb option 68 generates confidence interval for the slope and intercept. 69 The p option generates fitted values and standard errors. 70 The r option does some residual analysis (i.e., check 71 assumptions). The output statement generates a new data set 72 that contains the residuals and predicted/fitted values. The 73 id statement adds the variable specified to the fitted values output; NOTE: There were 48 observations read from the data set WORK.DIAMONDS1. NOTE: PROCEDURE GPLOT used (Total process time): 0.50 seconds 0.34 seconds 74 proc reg data=diamonds; model price=weight/clb p r; 75 output out=diag p=pred r=resid; 76 id weight; run; 77 NOTE: The data set WORK.DIAG has 49 observations and 4 variables. NOTE: PROCEDURE REG used (Total process time): 5.65 seconds 1.15 seconds 15

78 proc print data=diag; run; NOTE: There were 49 observations read from the data set WORK.DIAG. NOTE: PROCEDURE PRINT used (Total process time): 0.08 seconds 0.01 seconds 79 *generates a residual plot to assess model assumptions; 80 *the following code is not necessary if ODS graphics is on; 81 symbol1 v=circle i=none; 82 title2 color=blue 'Residual Plot'; 83 axis2 label=(angle=90 'Residual'); 84 proc gplot data=diag; plot resid*weight / haxis=axis1 vaxis=axis2 vref=0; 85 where price ne.; 86 run; NOTE: 4 records written to D:\Users\lfindsen\gplot2.png. 87 quit; NOTE: There were 48 observations read from the data set WORK.DIAG. WHERE price not =.; NOTE: PROCEDURE GPLOT used (Total process time): 0.45 seconds 0.24 seconds 16